In recent years, communication services have expanded and increased in popularity around the world. Shopping has also evolved with the evolution of telecommunications technologies, and on-line shopping is now commonplace. Although on-line shopping can be for traditional goods or services, on-line shopping from a device is often related to shopping for content for downloading to the device. Examples of content include, without limitation, media, games, messaging, social networks, programs for execution on the terminal devices, and any other applications or information for use on or with a device.
On-line stores for downloadable products traditionally provide the following options to allow a user to discover available items: 1) search by keywords, 2) browse categories such as Games, Business, Lifestyle, Shopping, Travel & Local, etc., or sub-categories such as Top Paid, Top Free, recent arrival, 3) recommendations, based on (a) the store's pick, (b) the user's download history, or (c) context relevance. Products are then listed based on the number of downloads. Similar recommendation options have also been applied to on-line content stores offering various media content and to on-line stores for sale of traditional goods or services.
Prior approaches to provide recommendations included very large undertakings that often utilized vast arrays of systems. Processing large histories produced very complex mathematical matrixes, and often required large main frame computers that utilized enormous amounts of computing power to crunch out results. Such processing was done during down time batch processing, so customer recommendations were created whether or not they were needed to be displayed online. And when lists were presented online they were based on data available during the nightly batch processing and did not represent the most current buying histories, essentially producing stale and inflexible lists.
Hence, a need exists for improved technologies for providing recommendations that efficiently provide a customer with up-to-date recommendations.